Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights

Abstract Over the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, precipitation nowcasting based on spatiotemporal extrapolation tends to provide a better precipitation forecast at shorter lead times with much less computation. Therefore, merging the precipitation forecasts from the NWP and extrapolation systems would be a viable approach to quantitative precipitation forecast (QPF). Although the optimal weights between the NWP and extrapolation systems are usually defined as a global constant, the weights would vary in space, particularly for global QPF. This study proposes a method to find the optimal weights at each location using the local threat score (LTS), a spatially localized version of the threat score. We test the locally optimal weighting with a global NWP system composed of the local ensemble transform Kalman filter and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM-LETKF). For the extrapolation system, the RIKEN’s global precipitation nowcasting system called GSMaP_RNC is used. GSMaP_RNC extrapolates precipitation patterns from the Japan Aerospace Exploration Agency (JAXA)’s Global Satellite Mapping of Precipitation (GSMaP). The benefit of merging in global precipitation forecast lasts longer compared to regional precipitation forecast. The results show that the locally optimal weighting is beneficial.

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Spin-up time from switching the microphysics scheme in the assimilation cycle and impacts on the precipitation forecast quality

10.5194/ems2021-379 ◽

2021 ◽

Author(s):

Sven Ulbrich ◽

Christian Welzbacher ◽

Kobra Khosravianghadikolaei ◽

Michael Hoff ◽

Alberto de Lozar ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Lead Time ◽

Weather Prediction ◽

Radar Data ◽

Precipitation Forecast ◽

Additional Measure ◽

Microphysics Scheme ◽

Numerical Weather ◽

Complementary Approach ◽

Forecast Quality

The SINFONY project at Deutscher Wetterdienst (DWD) aims to produce seamless precipitation forecast products from minutes up to 12 hours, with particular focus on convective events. While the near future predictions are typically from nowcasting procedures using radar data, the numerical weather prediction (NWP) aims at longer time scales. The lead-time in the latest available forecast is usually too long for merging both the nowcasting and NWP output to produce reliable seamless predictions.At DWD, the current forecasts are produced by the short range numerical weather prediction (SRNWP) making use of a continuous assimilation cycle with relatively long cutoff times and using 1-moment microphysics. In order to reduce the differences in the precipitation to the nowcasting on the NWP side, we use two different approaches. First, we reduce the lead-time from the model start by running 1-hourly forecasts based on an assimilation cycle with shorter data cutoff. Secondly, we use new observational systems in the assimilation cycle, such as radar or satellite data to capture and represent strong convective activity. This procedure is called Rapid Update Cycle (RUC). As an additional measure, we introduce a 2-Moment microphysics scheme into the numerical model, resulting in a better representation of the radar reflectivities. In order to keep the model state similar to that of the SRNWP, the RUC is a time limited assimilation cycle starting from forecasts of the SRNWP at pre-defined times.The introduction of the 2-Moment scheme leads to a spin-up affecting both the assimilation cycle and the short forecasts. The resulting effects are analysed by comparison with the corresponding assimilation cycle using the 1-Moment scheme. As a complementary approach for the analysis, the routine cycle is run with the 2-Moment scheme. The forecast quality is used as a measure to compare the results with respect to precipitation and additional observed parameters. It is shown in how far the resulting improvements are related to the assimilation and momentum scheme, or to the higher frequency of forecasts.

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Assessing the impact of Argo floats temperature measurements on the numerical weather prediction forecast skill

Mediterranean Marine Science ◽

10.12681/mms.15423 ◽

2019 ◽

Vol 20 (2) ◽

pp. 331 ◽

Cited By ~ 4

Author(s):

GEORGE VARLAS ◽

PETROS KATSAFADOS ◽

GERASIMOS KORRES ◽

ANASTASIOS PAPADOPOULOS

Keyword(s):

Boundary Conditions ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Lower Boundary ◽

Forecast Skill ◽

Temperature Measurements ◽

Precipitation Forecast ◽

Numerical Weather ◽

Argo Floats ◽

The Impact

The forecast skill of numerical weather prediction (NWP) models relies, among other factors such as the prediction itself and the assimilation scheme, on the accuracy of the observations utilized in the assimilation systems for the production of initial and boundary conditions. One of the most crucial parameters in weather forecasting is the sea surface temperature (SST). In the majority of NWP models, the initial and lower boundary conditions involve gridded (SST) analyses which consist of data obtained by buoys, ships and satellites. The main aim of this study is to integrate Argo temperature measurements in gridded SST analyses and to assess their impact on the forecast skill of a limited area atmospheric model. Argo floats are “state-of-the-art” oceanographic instruments producing high-quality temperature profiles for the ice-free ocean. In this study, Argo temperatures are incorporated into gridded SST fields without applying any smoothing method in order to directly assess the impact of Argo temperatures on numerical weather prediction. Their impact is assessed under intense weather cyclonic conditions at the Mediterranean Sea by performing two sensitivity simulations either incorporating or not Argo temperatures into gridded SST fields used in the generation of the initial and lower boundary conditions. The results indicate that the inclusion of Argo-measured near-surface temperatures in the lower boundary condition modifies the surface heat fluxes, thus affecting mean sea level pressure and precipitation. In particular, an overall improvement of the precipitation forecast skill up to 3% has been demonstrated. Moreover, the incorporation of Argo temperatures affects the simulated track and intensity of the cyclone over the Balkan Peninsula.

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A DEEP-LEARNING METHOD TO CORRECT THE OUTPUT OF A NUMERICAL WEATHER PREDICTION MODEL USED FOR SHORT-TERM PRECIPITATION FORECAST

Journal of Japan Society of Civil Engineers Ser G (Environmental Research) ◽

10.2208/jscejer.75.i_33 ◽

2019 ◽

Vol 75 (5) ◽

pp. I_33-I_39

Author(s):

Takeru KURAKAMI ◽

Kazuyoshi SOUMA ◽

Takashi MIYAMOTO ◽

Takahiko FURUYA ◽

Jun MAGOME ◽

...

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Precipitation Forecast ◽

Learning Method ◽

Short Term ◽

Numerical Weather

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Statistical Verification of Numerical Weather Prediction (NWP) for Rainfall Estimation in the East Coast Region

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.11.15954 ◽

2018 ◽

Vol 7 (3.11) ◽

pp. 168

Author(s):

Aisar Ashra M. Ashri ◽

Wardah Tahir ◽

Nurul Syahira M. Harmay ◽

Intan Shafeenar A. Mohtar ◽

Sazali Osman ◽

...

Keyword(s):

Numerical Weather Prediction ◽

East Coast ◽

Weather Prediction ◽

Peninsular Malaysia ◽

Probability Of Detection ◽

Rainfall Data ◽

Precipitation Forecast ◽

Mitigation Measures ◽

Numerical Weather ◽

Coast Region

Intense hydrological event such as floods are increasing lately especially in Peninsular Malaysia. Therefore, it is important to forecast the intense rainfall as part of flood preparedness and mitigation measures. In this study, Numerical Weather Prediction (NWP) model precipitation outputs using Weather Research and Forecasting (WRF) with horizontal resolution of 3 km have been validated against observed rainfall data measurements for its performance measurement. Forecasted rainfall event data of three (3) states in the East Coast Region; Kelantan, Terengganu and Pahang were evaluated and compared with the observed rainfall data before statistically verifying their accuracy using False Alarm Ratio (FAR) and Probability of Detection (POD). The results indicate a very promising potential of the models in producing quantitative precipitation forecast (QPF) for flood forecasting purpose in Kelantan, Terengganu and Pahang. Since these three states, which are located in the East Coast region of Peninsular Malaysia experienced annual flood event, accurate forecast rainfall data can be used to improve forecast information for flood indicator.

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Estimation of GNSS tropospheric products and their meteorological exploitation in Slovakia

Contributions to Geophysics and Geodesy ◽

10.31577/congeo.2020.50.1.5 ◽

2020 ◽

Vol 50 (1) ◽

pp. 83-111

Author(s):

Martin Imrišek ◽

Mária Derková ◽

Juraj Janák

Keyword(s):

Prediction Model ◽

Numerical Weather Prediction ◽

Initial Conditions ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Precipitation Forecast ◽

Total Delay ◽

Numerical Weather ◽

Zenith Total Delay ◽

The Impact

This paper discusses the in near–real time processing of Global Navigation Satellite System observations at the Department of Theoretical Geodesy at the Slovak University of Technology in Bratislava. Hourly observations from Central Europe are processed with 30 minutes delay to provide tropospheric products. The time series and maps of tropospheric products over Slovakia are published online. Zenith total delay is the most important tropospheric parameter. Its comparison with zenith total delays from IGS and E–GVAP solutions and the validation of estimated zenith total delay error over year 2018 have been made. Zenith total delays are used to improve initial conditions of numerical weather prediction model by the means of the three–dimensional variational analysis at Slovak Hydrometeorological Institute. The impact of assimilation of different observation types into numerical weather prediction model is discussed. The case study was performed to illustrate the impact of zenith total delay assimilation on the precipitation forecast.

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Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction

Monthly Weather Review ◽

10.1175/mwr-d-20-0419.1 ◽

2021 ◽

Author(s):

Deming Meng ◽

Yaodeng Chen ◽

Jun Li ◽

Hongli Wang ◽

Yuanbing Wang ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Weather Prediction ◽

Precipitation Forecast ◽

Background Error ◽

Single Observation ◽

Numerical Weather ◽

Background Error Covariance ◽

Error Covariance ◽

Analysis Scheme ◽

The Impact

AbstractThe background error covariance (B) behaves differently and needs to be carefully defined in cloudy areas due to larger uncertainties caused by models’ inability to correctly represent complex physical processes. This study proposes a new cloud-dependent B strategy by adaptively adjusting the hydrometeor-included B in the cloudy areas according to the cloud index (CI) derived from the satellite-based cloud products. The adjustment coefficient is determined by comparing the error statistics of B for the clear and cloudy areas based on the two-dimensional geographical masks. The comparison highlights the larger forecast errors and manifests the necessity of using appropriate B in cloudy areas. The cloud-dependent B is then evaluated by a series of single observation tests and three-week cycling assimilation and forecasting experiments. The single observation experiments confirm that the cloud-dependent B allows cloud dependency for the multivariate analysis increments and alleviates the discontinuities at the cloud mask borders by treating the CI as an exponent. The impact study on regional numerical weather prediction (NWP) demonstrates that the application of the cloud-dependent B reduces analyses and forecasts bias and increases precipitation forecast skills. Diagnostics of a heavy rainfall case indicate that the application of the cloud-dependent B enhances the moisture, wind, and hydrometeors analyses and forecasts, resulting in more accurate forecasts of accumulated precipitation. The cloud-dependent piecewise analysis scheme proposed herein is extensible, and a more precise definition of CI can improve the analysis, which deserves future investigation.

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Impact of model resolution on numerical weather prediction

MATEC Web of Conferences ◽

10.1051/matecconf/201928607012 ◽

2019 ◽

Vol 286 ◽

pp. 07012

Author(s):

Z. Sahlaoui ◽

S. Mordane

Keyword(s):

Numerical Weather Prediction ◽

Weather Prediction ◽

Precipitation Forecast ◽

Model Resolution ◽

Precipitation Field ◽

Numerical Weather ◽

North East ◽

The North ◽

The Impact

Several model configurations are used in Morocco for numerical weather prediction (NWP). The aim of this work is to verify the impact of resolution on the quality of models forecast, particularly the precipitation field. Three model configurations are tested with 7.5 km, 5 km and 2.5 km resolution. A rainy event over the North-East of Morocco is studied. The impact on models performances is assessed through the comparison of precipitation forecasts with the adjusted quantitative precipitation estimate from weather radar. The results show that the model with 2.5 km resolution gives the best quality precipitation forecast in term of both intensity and localisation.

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Assessment of New Satellite Missions within the Framework of Numerical Weather Prediction

Remote Sensing ◽

10.3390/rs12101580 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1580 ◽

Cited By ~ 3

Author(s):

Stuart Newman ◽

Fabien Carminati ◽

Heather Lawrence ◽

Niels Bormann ◽

Kirsti Salonen ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Gap Analysis ◽

Weather Prediction ◽

Data Sets ◽

Next Generation ◽

Numerical Weather ◽

Surface Emission ◽

Precipitation Measurement ◽

Microwave Imager ◽

Global Precipitation

Confidence in the use of Earth observations for monitoring essential climate variables (ECVs) relies on the validation of satellite calibration accuracy to within a well-defined uncertainty. The gap analysis for integrated atmospheric ECV climate monitoring (GAIA-CLIM) project investigated the calibration/validation of satellite data sets using non-satellite reference data. Here, we explore the role of numerical weather prediction (NWP) frameworks for the assessment of several meteorological satellite sensors: the advanced microwave scanning radiometer 2 (AMSR2), microwave humidity sounder-2 (MWHS-2), microwave radiation imager (MWRI), and global precipitation measurement (GPM) microwave imager (GMI). We find departures (observation-model differences) are sensitive to instrument calibration artefacts. Uncertainty in surface emission is identified as a key gap in our ability to validate microwave imagers quantitatively in NWP. The prospects for NWP-based validation of future instruments are considered, taking as examples the microwave sounder (MWS) and infrared atmospheric sounding interferometer-next generation (IASI-NG) on the next generation of European polar-orbiting satellites. Through comparisons with reference radiosondes, uncertainties in NWP fields can be estimated in terms of equivalent top-of-atmosphere brightness temperature. We find NWP-sonde differences are consistent with a total combined uncertainty of 0.15 K for selected temperature sounding channels, while uncertainties for humidity sounding channels typically exceed 1 K.

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